Comparative Performance of the Jaya Algorithm and NSGA-II in Multiobjective Irrigation Water Resource Planning

This study investigates the application of the Jaya algorithm as a simplified and efficient approach for solving multiobjective optimization problems in irrigation water resource planning. The analysis focuses on a real-world irrigation system in Bangladesh, where it is crucial to balance agricultural productivity with ecological water requirements, especially during periods of water scarcity. A previously established modeling structure was adopted, incorporating the same hydrological, agronomic, and economic input parameters, along with identical system constraints and optimization objectives. Unlike evolutionary algorithms that depend on control parameters or genetic operators, the Jaya algorithm offers a simple approach that does not require calibration parameters, such as mutation rate, crossover probability, or learning factors, to derive optimal water allocation strategies aimed at maximizing net returns and minimizing environmental flow deficits. The optimization results demonstrate that the Jaya algorithm effectively generates a well-spread Pareto front, capturing the trade-offs between conflicting water demands. Compared to conventional evolutionary techniques for similar conditions, Jaya showed equivalent economic outcomes and, in some cases, greater satisfaction with ecological needs. The study also reveals that fluctuations in rainfall patterns have a more pronounced impact on both economic returns and ecological outcomes than do variations in reservoir inflow. Overall, the research confirms the practicality of the Jaya algorithm for integrated water resource planning and emphasizes its simplicity and ease of use, making it a promising solution for sustainable irrigation water resource planning in regions vulnerable to climate variability.